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:2019,28(4):218-224
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机器学习实现心肌梗死的自动检测
(1.中国科学院大学, 北京 100049;2.中国科学院 沈阳计算技术研究所, 沈阳 110168;3.东北大学 计算机科学与工程学院, 沈阳 110004)
Machine Learning Achieves Automatic Detection of Myocardial Infarction
(1.University of Chinese Academy of Sciences, Beijing 110049, China;2.Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China;3.School of Computer Science and Engineering, Northeastern University, Shenyang 110004, China)
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投稿时间:2018-10-11    修订日期:2018-10-30
中文摘要: 心肌梗死是心肌细胞缺血性坏死,由于坏死程度不同,在心电图上表现不一,因此无法精确制定统一的评判标准.由于非典型心肌梗死特征不明显,目前的方法检测精度较低.本文针对心肌梗死类型的不同,为了提高非典型心肌梗死检测的准确率并且降低典型心肌梗死的时间复杂度,提出了BP神经网络与卷积神经网络结合的方法.首先,将原始ECG信号经过均值滤波平滑,去基线等处理,提取相关特征值,对于典型心肌梗死使用BP神经网络训练分类,而对于特征不明显非典型心肌梗死,按固定长度截取后利用卷积神经网络进行训练.为了进一步提高准确率,结合12导联数据进行分析.实验结果表明,该方法在心肌梗死的检测上,相对于其他方法提高了2%,特别是在非典型心肌梗死检测上明显改善.
中文关键词: ECG  均值滤波  心肌梗死  神经网络
Abstract:Myocardial infarction is a vascular necrosis of myocardial cells. Because of the different degrees of necrosis, the performance on the electrocardiogram is different. Because the characteristics of atypical myocardial infarction are not obvious, the current method has low detection accuracy. In this study, a method combining BP neural network and convolutional neural network is proposed according to the type of disease to improve the accuracy of detecting atypical myocardial infarction and reduce the time complexity of typical myocardial infarction. First, the original ECG signal is filtered, smoothed, de-baselined, etc., and the relevant feature values are extracted. The BP neural network is used to train the classification. For the features that are not obvious, the convolutional neural network is used for training after intercepting at a fixed length. In order to further improve the accuracy, the 12-lead data was combined for analysis. The experimental results show that this method improves the detection of myocardial infarction by 2% compared with other methods, especially in the detection of atypical myocardial infarction.
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常战国,蒲宝明,李相泽,王帅,杨朔.机器学习实现心肌梗死的自动检测.计算机系统应用,2019,28(4):218-224
CHANG Zhan-guo,PU Bao-ming,LI Xiang-ze,WANG Shuai,YANG Shuo.Machine Learning Achieves Automatic Detection of Myocardial Infarction.COMPUTER SYSTEMS APPLICATIONS,2019,28(4):218-224

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